Chaojun Wang (Stanford University)
Darrell Duffie (Stanford University)
Bilateral Contracting in Network Financial Markets
We model bargaining in over-the-counter network markets over the terms and prices of contracts. Of concern is whether bilateral non-cooperative bargaining is sufficient to achieve efficiency in this multilateral setting. For example, will market participants assign insolvency-based seniority in a socially efficient manner, or should bankruptcy laws override contractual terms with an automatic stay? We provide conditions under which bilateral bargaining over contingent contracts is efficient for a network of market participants. Examples include seniority assignment, close-out netting and collateral rights, secured debt liens, and leverage-based covenants. Given the ability to use covenants and other contingent contract terms, central market participants efficiently internalize the costs and benefits of their counterparties through the pricing of contracts. We provide counterexamples to efficiency for less contingent forms of bargaining coordination.
Hannes Hoffmann (Ludwig-Maximilians-Universität München)
Risk-Consistent Conditional Systemic Risk Measures
Our studies are based on the axiomatic characterization in Chen et al. (2013) of a similar class of systemic risk measures in a finite state and unconditional framework.
Our contributions are:
Jose-Henry Leon (CLS Bank International)
Network Analysis in FX settlement
An overview of some Network Theory metrics and concepts is given. A proposal for applying Network analytics to the FX settlement process in a FX Settlement system is considered. In particular I explore the used of Centrality measures to analyze the payments of financial institutions when trading through a bilateral Network. As an application of these concepts, an analysis of the bilateral trades in a FX settlement system is done. I used metrics like the degree distribution, betweenness centrality and connectivity. These metrics give a way to characterize part of the FX settlement process, study the connectivity of the members in the settlement network, get a sense of their evolution along time and measure their network topology variability. These centrality metrics give a clear individual and structural description of the financial institutions’ bilateral relationships by means of net trade’s amounts that settle in the FX settlement system. I analyze the application of the network community detection algorithm created by Blondel et al.  to the FX settlement network so to explore relations between the communities and the size of default cascades if there were present in the Network. Another interest-ing quantity that is analyzed here is the Soramaki’s et al.  SinkRank metric. This metric gives a measure of the importance of a given bank in the FX Network in case it defaults; I used this metric to build a ranking of the 63 most important Banks trading on FX in terms of their systemic risk importance.
Amirhossein Sadoghi (Frankfurt School of Finance & Management)
Measuring Systemic Risk: Robust Ranking Techniques Approach
The recent economic crisis has raised a wide awareness that the financial system should be viewed as a complex network with financial institutions and financial dependencies respectively as nodes and links between these nodes.
Systemic risk is defined as the risk of default of a large portion of financial exposures among institution in the network. Indeed, the structure of this network is an important element to measure systemic risk and to determine the systemically important nodes in a large financial network. Typically in the real-world financial network, there are some disconnected subgraphs as well as cycles and current solutions for cyclic matrix it may converge slowly and not optimum.
In this research, we introduce a metric for systemic risk measurement with taking into account both common idiosyncratic shocks as well as contagion through counterparty exposures.
Our focus is on application of Eigenvalue problems, as a robust approach to the ranking techniques, to measure systemic risk.
We show how the Eigenvalue problem reduces to a non-smooth convex optimization problem and it can be solved in the efficient way. We applied this technique and studied the performance and convergence behavior of the algorithm with different structure of the financial network. Moreover, we analyzed some financial regulations to mitigate the probability of failure based on both node and link structure of the network.